FeDMRA: 联邦增量学习中的动态记忆回放分配
FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation
AI总结：
本文提出动态内存分配策略，以应对联邦医疗系统中非独立同分布数据带来的挑战，通过合理分配存储资源提升模型性能。
摘要：
在联邦医疗系统中，联邦类增量学习（FCIL）已成为关键范式，使分布式客户端能够在保护数据隐私的同时实现持续适应性模型学习。然而，实际应用中，分布式框架内的数据往往具有非独立同分布（non-IID）特性，使传统持续学习方法不适用。为解决这些挑战，本文覆盖了更全面的增量任务场景，并提出了一种基于数据回放机制的动态内存分配策略，以充分利用数据异质性，同时考虑所有参与客户端的性能公平性，从而建立一个平衡且适应性的解决方案，以缓解灾难性遗忘。与客户端示例内存的固定分配不同，所提出的方法强调在客户端之间合理分配有限的存储资源，以提高模型性能。此外，本文在三个医学图像数据集上进行了广泛实验，结果表明与现有基线模型相比，性能有显著提升。
英文摘要：
In federated healthcare systems, Federated Class-Incremental Learning (FCIL) has emerged as a key paradigm, enabling continuous adaptive model learning among distributed clients while safeguarding data privacy. However, in practical applications, data across agent nodes within the distributed framework often exhibits non-independent and identically distributed (non-IID) characteristics, rendering traditional continual learning methods inapplicable. To address these challenges, this paper covers more comprehensive incremental task scenarios and proposes a dynamic memory allocation strategy for exemplar storage based on the data replay mechanism. This strategy fully taps into the inherent potential of data heterogeneity, while taking into account the performance fairness of all participating clients, thereby establishing a balanced and adaptive solution to mitigate catastrophic forgetting. Unlike the fixed allocation of client exemplar memory, the proposed scheme emphasizes the rational allocation of limited storage resources among clients to improve model performance. Furthermore, extensive experiments are conducted on three medical image datasets, and the results demonstrate significant performance improvements compared to existing baseline models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
